Hcnnxgboost: A Hybrid Cnn-Xgboost Approach for Effective Emotion Detection in Textual Data
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in recent years, emotional analysis has become a key focus in computational studies, driven by the need to understand societal sentiments. this exploration is motivated by its many promising applications, such as community well-being evaluation, human-computer interaction, suicide prevention, and personalized recommendations. even with progress in other areas like identifying expressions from facial cues and speech, the study of text-based emotion recognition remains a fascinating field of research because machines struggle to interpret context, especially compared to human capabilities. our research addresses this by introducing a multiclass text-based emotion detection system that combines a cnn architecture with xgboost for improved classification in natural language processing. we pre-process publicly available datasets and use glove pre-trained word embeddings for better text representation. a major contribution of our work is enhancing the feature space by combining cnn probabilities with the original text data. the proposed hcnnxgboost model outperforms all other machine learning and deep learning algorithms across the emoint, isear, and crowdflower datasets, achieving f-scores of 90.1%, 87.4%, and 62.2%, respectively. experimental evaluations on benchmark datasets show better f-scores, confirming the effectiveness of our approach. comparisons with other classifiers highlight the enhanced performance and effectiveness of our hybrid cnnxgboost (hcnnxgboost) model, making it one of the best solutions for emotion classification in natural language processing tasks.
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